[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123236-en":3,"doc-seo-123236-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123236,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine Learning based Identification of Cancer Related MiRNA and Gene Biomarkers","Cancer remains a leading cause of death, making reliable biomarker discovery essential for accurate diagnosis. Using TCGA data, the study evaluates both machine learning and deep learning models to identify cancer-associated genes and miRNAs. Supervised methods such as Random Forest, SVM, and Gradient Boosting are compared with CNNs and LSTM networks, and graph-based approaches are used to explore miRNA–gene relationships. Models are validated via K-fold cross-validation with metrics including accuracy, precision, recall, F1, and AUC. Deep learning, especially CNN (AUC=0.95) and LSTM (AUC=0.93), performs best, supporting precision oncology applications.","Machine Learning based Identification of Cancer Related Mirna and Gene Biomarkers  \n1Karare Bharati Arunrao, 2Dr. Priya Vij  \n1,2 Department of Computer Science and Engineering, Kalinga University, Naya Raipur, Chhattisgarh, India  \nAbstract  \nCancer is still a major killer, thus finding effective biomarkers and making correct diagnoses requires cutting-edge computational methods. Using data from The Cancer Genome Atlas (TCGA), this study investigates the predictive power of deep learning and machine learning models in finding genes and miRNAs unique to cancer. Supervised learning models including Random Forest, Support Vector Machine (SVM), and Gradient Boosting were used in a thorough examination, along with deep learning architectures like CNNs and LSTM networks. To further understand the intricate relationships between miRNAs and genes, we used graph-based approaches. Using important performance indicators including accuracy, precision, recall, F1 score, and Area under the Curve (AUC), the models were assessed using K-fold cross-validation. For the purpose of differentiating cancer-specific biomarkers, the findings showed that deep learning models, especially CNN (AUC = 0.95) and LSTM (AUC = 0.93), performed better than conventional machine learning methods. By providing data-driven approaches for early identification and individualized treatment regimens, this work highlights the potential of deep learning to advance precision oncology.  \nKeywords: Gene expression, Machine Learning, Biomarker, Tumor, Immune  \nI.INTRODUCTION  \nEvery year, cancer claims the lives of millions of people throughout the world, making it one of the biggest obstacles in contemporary medicine. A complex and diverse illness, it is defined by mutations in genes, interactions with the microenvironment, and unchecked cell development. Biomarkers have been developed to assist in early detection, diagnosis, prognosis, and therapy selection as a result of developments in molecular biology and bioinformatics. Oneof the most important aspects of cancer research is the role of microRNAs (miRNAs) and gene expression patterns. In order to improve patient outcomes and therapy efficacy, identify trustworthy biomarkers is crucial for creating precision medicine strategies. Scalability, accuracy, and repeatability are three areas where conventional biomarker discovery approaches fall short. Machine learning (ML) has brought about a paradigm shift in this area by offering data-driven methods for evaluating intricate genetic data, discovering possible biomarkers, and making highly accurate predictions of cancer-related patterns.  \ntiny non-coding RNA molecules called microRNAs  \n(miRNAs) target messenger RNAs (mRNAs) for destruction or translational suppression; they are essential regulators of gene expression. As a result of their abnormal expression ina number of malignancies, these molecules are promising biomarkers for use in diagnosis, prognosis, and treatment planning.  \nBy affecting critical cellular pathways including apoptosis, proliferation, metastasis, and immune response, miRNAs can play the role of oncogenes or tumor suppressors. Their capacity to remain stable in common body fluids including blood and urine increases their promise as non-invasive cancer diagnostic tools. Despite their potential, the massive amounts of data produced by high-throughput sequencing technology make it difficult to identify certain miRNAslinked with various cancer types. Machine learning approaches shine in this area because they use computational and statistical methods to rapidly analyze massive information, find hidden patterns, and categorize pertinent biomarkers.  \nIn a similar vein, gene expression profiling has emerged as an essential tool in the fight against cancer by illuminating the molecular pathways that drive tumorigenesis and metastasis. Differentiating between cancer subtypes, tracking disease progression, and measuring therapy efficacy are all made easier b","cbCaisKySQ48TUkM","https://ap.wps.com/l/cbCaisKySQ48TUkM","pdf",463998,1,"English","en",105,"# Introduction\n## Role of microRNAs and gene expression in cancer\n## Limits of conventional biomarker discovery and value of ML\n# Biological Functions of miRNA in Cancer\n## miRNA involvement in cancer progression","[{\"question\":\"Which models are used to identify cancer-related miRNA and gene biomarkers?\",\"answer\":\"The study uses supervised learning models including Random Forest, SVM, and Gradient Boosting, along with deep learning architectures such as CNN and LSTM. It also applies graph-based approaches to understand miRNA–gene relationships.\"},{\"question\":\"How are the models evaluated in the study?\",\"answer\":\"Performance is measured using accuracy, precision, recall, F1 score, and AUC. K-fold cross-validation is used to assess model generalization.\"},{\"question\":\"What do the results indicate about deep learning compared with conventional machine learning?\",\"answer\":\"Deep learning models perform better for cancer-specific biomarker differentiation. In particular, CNN achieves AUC=0.95 and LSTM achieves AUC=0.93, outperforming conventional methods.\"}]","Machine Learning based Identification of Cancer Related MiRNA and Gene Biomarkers | PDF",1785815378,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"machine-learning-based-identification-of-cancer-related-mirna-and-gene-biomarkers","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-based-identification-of-cancer-related-mirna-and-gene-biomarkers/123236/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Which models are used to identify cancer-related miRNA and gene biomarkers?","Question",{"text":74,"@type":75},"The study uses supervised learning models including Random Forest, SVM, and Gradient Boosting, along with deep learning architectures such as CNN and LSTM. It also applies graph-based approaches to understand miRNA–gene relationships.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How are the models evaluated in the study?",{"text":79,"@type":75},"Performance is measured using accuracy, precision, recall, F1 score, and AUC. K-fold cross-validation is used to assess model generalization.",{"name":81,"@type":72,"acceptedAnswer":82},"What do the results indicate about deep learning compared with conventional machine learning?",{"text":83,"@type":75},"Deep learning models perform better for cancer-specific biomarker differentiation. In particular, CNN achieves AUC=0.95 and LSTM achieves AUC=0.93, outperforming conventional methods.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]